{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Building the Hank.ai Darknet/YOLO Framework\n",
        "- Using the [Hank.ai Darknet/YOLO repo](https://github.com/hank-ai/darknet#table-of-contents)\n",
        "- Based on https://github.com/hank-ai/darknet#linux-cmake-method\n",
        "- See the [Darknet/YOLO FAQ](https://www.ccoderun.ca/programming/yolo_faq/)\n",
        "- Last updated by Stéphane Charette on 2024-06-07 for Darknet v2.0-225\n",
        "- Remember to select:  Edit -> Notebook settings -> T4 GPU"
      ],
      "metadata": {
        "id": "ooUJBtxFJ3Ff"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install the dependencies"
      ],
      "metadata": {
        "id": "DZKBrI8nYav_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzCaepsPJkzZ",
        "outputId": "d18e965d-5837-4037-a5db-60888ab184fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "build-essential is already the newest version (12.9ubuntu3).\n",
            "cmake is already the newest version (3.22.1-1ubuntu1.22.04.2).\n",
            "git is already the newest version (1:2.34.1-1ubuntu1.11).\n",
            "libopencv-dev is already the newest version (4.5.4+dfsg-9ubuntu4+jammy0).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n",
            "libcudnn9-cuda-11 - cuDNN runtime libraries for CUDA 11.8\n",
            "libcudnn9-dev-cuda-11 - cuDNN development headers and symlinks for CUDA 11.8\n",
            "libcudnn9-samples - cuDNN samples\n",
            "libcudnn9-static-cuda-11 - cuDNN static libraries for CUDA 11.8\n",
            "libcudnn9-cuda-12 - cuDNN runtime libraries for CUDA 12.4\n",
            "libcudnn9-dev-cuda-12 - cuDNN development headers and symlinks for CUDA 12.4\n",
            "libcudnn9-static-cuda-12 - cuDNN static libraries for CUDA 12.4\n",
            "cuda-libraries-12-0 - CUDA Libraries 12.0 meta-package\n",
            "cuda-libraries-12-1 - CUDA Libraries 12.1 meta-package\n",
            "cuda-libraries-12-2 - CUDA Libraries 12.2 meta-package\n",
            "cuda-libraries-12-3 - CUDA Libraries 12.3 meta-package\n",
            "cuda-libraries-12-4 - CUDA Libraries 12.4 meta-package\n",
            "cuda-libraries-12-5 - CUDA Libraries 12.5 meta-package\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "cuda-libraries-dev-12-2 is already the newest version (12.2.2-1).\n",
            "libcudnn9-dev-cuda-12 is already the newest version (9.1.1.17-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "# First part will install the tools we'll use to build.\n",
        "!sudo apt-get install build-essential git libopencv-dev cmake\n",
        "\n",
        "# Next few lines is for NVIDIA software.\n",
        "!apt-cache search libcudnn9\n",
        "!apt-cache search cuda-libraries-12\n",
        "!sudo apt-get install libcudnn9-dev-cuda-12 cuda-libraries-dev-12-2"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Verifying NVIDIA"
      ],
      "metadata": {
        "id": "px_QergOYmvc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# There are 2 specific tools from NVIDIA we're going to use to verify that everything is installed.\n",
        "\n",
        "# The first is nvidia-smi which gives real-time information in the GPU.\n",
        "!nvidia-smi\n",
        "\n",
        "# Next is the nvidia compiler.  If you get a \"command not found\" error then things will not work!\n",
        "!nvcc --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZoCBDiOOMgd",
        "outputId": "31227f93-3de8-4ed7-ed7a-d036dd154cc6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Jun  7 13:59:02 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cloning the Darknet/YOLO repo"
      ],
      "metadata": {
        "id": "NhS_LMhhZ0h5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove any pre-existing repo files, and clone the Darknet/YOLO repo from Hank.ai.\n",
        "%mkdir -p ~/src\n",
        "%cd ~/src\n",
        "%rm -rf ~/src/darknet\n",
        "!git clone https://github.com/hank-ai/darknet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8ErqAEUJrz7",
        "outputId": "59030203-4dac-491a-8a0e-8468c32242bf"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/src\n",
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 17896, done.\u001b[K\n",
            "remote: Counting objects: 100% (2368/2368), done.\u001b[K\n",
            "remote: Compressing objects: 100% (735/735), done.\u001b[K\n",
            "remote: Total 17896 (delta 1741), reused 2236 (delta 1624), pack-reused 15528\u001b[K\n",
            "Receiving objects: 100% (17896/17896), 17.98 MiB | 14.10 MiB/s, done.\n",
            "Resolving deltas: 100% (12213/12213), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparing to build the Darknet/YOLO repo"
      ],
      "metadata": {
        "id": "38Of4wfKaSl5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the Darknet/YOLO build directory\n",
        "%cd ~/src/darknet\n",
        "%rm -rf ~/src/darknet/build\n",
        "%mkdir ~/src/darknet/build\n",
        "%cd ~/src/darknet/build\n",
        "!cmake -DCMAKE_BUILD_TYPE=Release ..\n",
        "\n",
        "# Once cmake has finished, make sure that CUDA and cuDNN are working.  The output should contain lines such as:\n",
        "#     \"CUDA detected. Darknet will use the GPU.\"\n",
        "# and\n",
        "#     \"Enabling cuDNN\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LlVwsQdrM6GI",
        "outputId": "837375ee-a015-4d3c-eaa2-1aec75cacbea"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/src/darknet\n",
            "/root/src/darknet/build\n",
            "-- Darknet v2.0-225-g277ed9f4-dirty\n",
            "-- The C compiler identification is GNU 11.4.0\n",
            "-- The CXX compiler identification is GNU 11.4.0\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Check for working C compiler: /usr/bin/cc - skipped\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "-- Looking for a CUDA compiler\n",
            "-- Looking for a CUDA compiler - /usr/local/cuda/bin/nvcc\n",
            "-- CUDA detected. Darknet will use the GPU.\n",
            "-- The CUDA compiler identification is NVIDIA 12.2.140\n",
            "-- Detecting CUDA compiler ABI info\n",
            "-- Detecting CUDA compiler ABI info - done\n",
            "-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped\n",
            "-- Detecting CUDA compile features\n",
            "-- Detecting CUDA compile features - done\n",
            "-- Found CUDAToolkit: /usr/local/cuda/include (found version \"12.2.140\") \n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
            "-- Found Threads: TRUE  \n",
            "-- Found cuDNN library: /usr/lib/x86_64-linux-gnu/libcudnn.so\n",
            "-- Found cuDNN include: /usr/include\n",
            "-- Hardware is 32-bit or 64-bit, and seems to be Intel or AMD:  x86_64\n",
            "-- Found Threads \n",
            "-- Found OpenCV: /usr (found version \"4.5.4\") \n",
            "-- Found OpenCV 4.5.4\n",
            "-- Found OpenMP \n",
            "-- Enabling AVX and SSE optimizations.\n",
            "-- Making an optimized release build.\n",
            "-- Skipping Doxygen (not found)\n",
            "-- Setting up DARKNET OBJ\n",
            "-- Setting up DARKNET LIB\n",
            "-- Setting up DARKNET CLI\n",
            "-- Configuring done (5.5s)\n",
            "-- Generating done (0.0s)\n",
            "-- Build files have been written to: /root/src/darknet/build\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building and installing Darknet/YOLO files"
      ],
      "metadata": {
        "id": "afhxH-K1atAQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build Darknet/YOLO\n",
        "%cd ~/src/darknet/build\n",
        "!make -j $(nproc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PB5r5gHVOu9",
        "outputId": "01ce955b-ca82-4260-8e47-365923594464"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/src/darknet/build\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -S/root/src/darknet -B/root/src/darknet/build --check-build-system CMakeFiles/Makefile.cmake 0\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_progress_start /root/src/darknet/build/CMakeFiles /root/src/darknet/build//CMakeFiles/progress.marks\n",
            "make  -f CMakeFiles/Makefile2 all\n",
            "make[1]: Entering directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknetobjlib.dir/build.make src-lib/CMakeFiles/darknetobjlib.dir/depend\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-lib /root/src/darknet/build /root/src/darknet/build/src-lib /root/src/darknet/build/src-lib/CMakeFiles/darknetobjlib.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknetobjlib.dir/build.make src-lib/CMakeFiles/darknetobjlib.dir/build\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "[  2%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/Chart.cpp.o\u001b[0m\n",
            "[  2%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/Timing.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/Chart.cpp.o -MF CMakeFiles/darknetobjlib.dir/Chart.cpp.o.d -o CMakeFiles/darknetobjlib.dir/Chart.cpp.o -c /root/src/darknet/src-lib/Chart.cpp\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/Timing.cpp.o -MF CMakeFiles/darknetobjlib.dir/Timing.cpp.o.d -o CMakeFiles/darknetobjlib.dir/Timing.cpp.o -c /root/src/darknet/src-lib/Timing.cpp\n",
            "[  3%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/activation_kernels.cu -o CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o\n",
            "[  4%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o -c /root/src/darknet/src-lib/activation_layer.cpp\n",
            "[  5%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/activations.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/activations.cpp.o -MF CMakeFiles/darknetobjlib.dir/activations.cpp.o.d -o CMakeFiles/darknetobjlib.dir/activations.cpp.o -c /root/src/darknet/src-lib/activations.cpp\n",
            "[  6%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/art.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/art.cpp.o -MF CMakeFiles/darknetobjlib.dir/art.cpp.o.d -o CMakeFiles/darknetobjlib.dir/art.cpp.o -c /root/src/darknet/src-lib/art.cpp\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/activations.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat gradient(float, ACTIVATION)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/activations.cpp:362:44:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis statement may fall through [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wimplicit-fallthrough=\u0007-Wimplicit-fallthrough=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  362 |                         \u001b[01;35m\u001b[Kdarknet_fatal_error(DARKNET_LOC, \"should be used custom NORM_CHAN or NORM_CHAN_SOFTMAX-function for gradient\")\u001b[m\u001b[K;\n",
            "      |                         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/activations.cpp:363:17:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Khere\n",
            "  363 |                 \u001b[01;36m\u001b[Kcase\u001b[m\u001b[K ELU:\n",
            "      |                 \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "[  8%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o -c /root/src/darknet/src-lib/avgpool_layer.cpp\n",
            "[  9%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/avgpool_layer_kernels.cu -o CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o\n",
            "[ 10%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o -c /root/src/darknet/src-lib/batchnorm_layer.cpp\n",
            "[ 11%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/blas.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/blas.cpp.o -MF CMakeFiles/darknetobjlib.dir/blas.cpp.o.d -o CMakeFiles/darknetobjlib.dir/blas.cpp.o -c /root/src/darknet/src-lib/blas.cpp\n",
            "[ 12%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/blas_kernels.cu -o CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o\n",
            "[ 13%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/box.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/box.cpp.o -MF CMakeFiles/darknetobjlib.dir/box.cpp.o.d -o CMakeFiles/darknetobjlib.dir/box.cpp.o -c /root/src/darknet/src-lib/box.cpp\n",
            "[ 15%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/captcha.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/captcha.cpp.o -MF CMakeFiles/darknetobjlib.dir/captcha.cpp.o.d -o CMakeFiles/darknetobjlib.dir/captcha.cpp.o -c /root/src/darknet/src-lib/captcha.cpp\n",
            "[ 16%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/cifar.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/cifar.cpp.o -MF CMakeFiles/darknetobjlib.dir/cifar.cpp.o.d -o CMakeFiles/darknetobjlib.dir/cifar.cpp.o -c /root/src/darknet/src-lib/cifar.cpp\n",
            "[ 17%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/classifier.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/classifier.cpp.o -MF CMakeFiles/darknetobjlib.dir/classifier.cpp.o.d -o CMakeFiles/darknetobjlib.dir/classifier.cpp.o -c /root/src/darknet/src-lib/classifier.cpp\n",
            "[ 18%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/coco.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/coco.cpp.o -MF CMakeFiles/darknetobjlib.dir/coco.cpp.o.d -o CMakeFiles/darknetobjlib.dir/coco.cpp.o -c /root/src/darknet/src-lib/coco.cpp\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/classifier.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid train_classifier(char*, char*, char*, int*, int, int, int, int, int, int, char*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/classifier.cpp:217:72:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%lld\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Klong long int\u001b[m\u001b[K’, but argument 8 has type ‘\u001b[01m\u001b[Kuint64_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  217 |                 printf(\"%d, %.3f: %f, %f avg, %f rate, %lf seconds, \u001b[01;35m\u001b[K%lld\u001b[m\u001b[K images, %f hours left\\n\", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), \u001b[32m\u001b[K*net.seen\u001b[m\u001b[K, avg_time);\n",
            "      |                                                                     \u001b[01;35m\u001b[K~~~^\u001b[m\u001b[K                                                                                                                                                   \u001b[32m\u001b[K~~~~~~~~~\u001b[m\u001b[K\n",
            "      |                                                                        \u001b[01;35m\u001b[K|\u001b[m\u001b[K                                                                                                                                                   \u001b[32m\u001b[K|\u001b[m\u001b[K\n",
            "      |                                                                        \u001b[01;35m\u001b[Klong long int\u001b[m\u001b[K                                                                                                                                       \u001b[32m\u001b[Kuint64_t {aka long unsigned int}\u001b[m\u001b[K\n",
            "      |                                                                     \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "[ 19%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/col2im.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/col2im.cpp.o -MF CMakeFiles/darknetobjlib.dir/col2im.cpp.o.d -o CMakeFiles/darknetobjlib.dir/col2im.cpp.o -c /root/src/darknet/src-lib/col2im.cpp\n",
            "[ 20%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/col2im_kernels.cu -o CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o\n",
            "[ 22%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/compare.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/compare.cpp.o -MF CMakeFiles/darknetobjlib.dir/compare.cpp.o.d -o CMakeFiles/darknetobjlib.dir/compare.cpp.o -c /root/src/darknet/src-lib/compare.cpp\n",
            "[ 23%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o -c /root/src/darknet/src-lib/connected_layer.cpp\n",
            "[ 24%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o -c /root/src/darknet/src-lib/conv_lstm_layer.cpp\n",
            "[ 25%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/convolutional_kernels.cu -o CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o\n",
            "[ 26%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o -c /root/src/darknet/src-lib/convolutional_layer.cpp\n",
            "[ 27%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o -c /root/src/darknet/src-lib/cost_layer.cpp\n",
            "[ 29%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o -c /root/src/darknet/src-lib/crnn_layer.cpp\n",
            "[ 30%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o -c /root/src/darknet/src-lib/crop_layer.cpp\n",
            "[ 31%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/crop_layer_kernels.cu -o CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o\n",
            "[ 32%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o -MF CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o.d -o CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o -c /root/src/darknet/src-lib/dark_cuda.cpp\n",
            "[ 33%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o -MF CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o.d -o CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o -c /root/src/darknet/src-lib/darknet_args_and_parms.cpp\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid pre_allocate_pinned_memory(size_t)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:444:47:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunknown conversion type character ‘\u001b[01m\u001b[K \u001b[m\u001b[K’ in format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  444 |                         printf(\" Allocated %ll\u001b[01;35m\u001b[K \u001b[m\u001b[Kpinned block \\n\", pinned_block_size);\n",
            "      |                                               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:444:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ktoo many arguments for format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-extra-args\u0007-Wformat-extra-args\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  444 |                         printf(\u001b[01;35m\u001b[K\" Allocated %ll pinned block \\n\"\u001b[m\u001b[K, pinned_block_size);\n",
            "      |                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat* cuda_make_array_pinned_preallocated(float*, size_t)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:466:57:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunknown conversion type character ‘\u001b[01m\u001b[K,\u001b[m\u001b[K’ in format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  466 |                         printf(\"\\n Pinned block_id = %ll\u001b[01;35m\u001b[K,\u001b[m\u001b[K filled = %f %% \\n\", pinned_block_id, filled);\n",
            "      |                                                         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:466:69:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%f\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kdouble\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’ {aka ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’} [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  466 |                         printf(\"\\n Pinned block_id = %ll, filled = \u001b[01;35m\u001b[K%f\u001b[m\u001b[K %% \\n\", \u001b[32m\u001b[Kpinned_block_id\u001b[m\u001b[K, filled);\n",
            "      |                                                                    \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "      |                                                                     \u001b[01;35m\u001b[K|\u001b[m\u001b[K         \u001b[32m\u001b[K|\u001b[m\u001b[K\n",
            "      |                                                                     \u001b[01;35m\u001b[Kdouble\u001b[m\u001b[K    \u001b[32m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K\n",
            "      |                                                                    \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:466:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ktoo many arguments for format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-extra-args\u0007-Wformat-extra-args\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  466 |                         printf(\u001b[01;35m\u001b[K\"\\n Pinned block_id = %ll, filled = %f %% \\n\"\u001b[m\u001b[K, pinned_block_id, filled);\n",
            "      |                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:481:78:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunknown conversion type character ‘\u001b[01m\u001b[K \u001b[m\u001b[K’ in format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  481 |                         printf(\"Try to allocate new pinned memory, size = %ll\u001b[01;35m\u001b[K \u001b[m\u001b[KMB \\n\", size / (1024 * 1024));\n",
            "      |                                                                              \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:481:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ktoo many arguments for format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-extra-args\u0007-Wformat-extra-args\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  481 |                         printf(\u001b[01;35m\u001b[K\"Try to allocate new pinned memory, size = %ll MB \\n\"\u001b[m\u001b[K, size / (1024 * 1024));\n",
            "      |                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:487:77:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunknown conversion type character ‘\u001b[01m\u001b[K \u001b[m\u001b[K’ in format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat=\u0007-Wformat=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  487 |                         printf(\"Try to allocate new pinned BLOCK, size = %ll\u001b[01;35m\u001b[K \u001b[m\u001b[KMB \\n\", size / (1024 * 1024));\n",
            "      |                                                                             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/dark_cuda.cpp:487:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ktoo many arguments for format [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wformat-extra-args\u0007-Wformat-extra-args\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  487 |                         printf(\u001b[01;35m\u001b[K\"Try to allocate new pinned BLOCK, size = %ll MB \\n\"\u001b[m\u001b[K, size / (1024 * 1024));\n",
            "      |                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "[ 34%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o -MF CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o.d -o CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o -c /root/src/darknet/src-lib/darknet_cfg_and_state.cpp\n",
            "[ 36%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o -MF CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o.d -o CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o -c /root/src/darknet/src-lib/darknet_format_and_colour.cpp\n",
            "[ 37%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o -MF CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o.d -o CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o -c /root/src/darknet/src-lib/darknet_utils.cpp\n",
            "[ 38%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/data.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/data.cpp.o -MF CMakeFiles/darknetobjlib.dir/data.cpp.o.d -o CMakeFiles/darknetobjlib.dir/data.cpp.o -c /root/src/darknet/src-lib/data.cpp\n",
            "[ 39%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/demo.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/demo.cpp.o -MF CMakeFiles/darknetobjlib.dir/demo.cpp.o.d -o CMakeFiles/darknetobjlib.dir/demo.cpp.o -c /root/src/darknet/src-lib/demo.cpp\n",
            "[ 40%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o -c /root/src/darknet/src-lib/detection_layer.cpp\n",
            "[ 41%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/detector.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/detector.cpp.o -MF CMakeFiles/darknetobjlib.dir/detector.cpp.o.d -o CMakeFiles/darknetobjlib.dir/detector.cpp.o -c /root/src/darknet/src-lib/detector.cpp\n",
            "[ 43%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/dice.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/dice.cpp.o -MF CMakeFiles/darknetobjlib.dir/dice.cpp.o.d -o CMakeFiles/darknetobjlib.dir/dice.cpp.o -c /root/src/darknet/src-lib/dice.cpp\n",
            "[ 44%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o -c /root/src/darknet/src-lib/dropout_layer.cpp\n",
            "[ 45%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/dropout_layer_kernels.cu -o CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o\n",
            "[ 46%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o -c /root/src/darknet/src-lib/gaussian_yolo_layer.cpp\n",
            "[ 47%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/gemm.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/gemm.cpp.o -MF CMakeFiles/darknetobjlib.dir/gemm.cpp.o.d -o CMakeFiles/darknetobjlib.dir/gemm.cpp.o -c /root/src/darknet/src-lib/gemm.cpp\n",
            "[ 48%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/go.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/go.cpp.o -MF CMakeFiles/darknetobjlib.dir/go.cpp.o.d -o CMakeFiles/darknetobjlib.dir/go.cpp.o -c /root/src/darknet/src-lib/go.cpp\n",
            "[ 50%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o -c /root/src/darknet/src-lib/gru_layer.cpp\n",
            "[ 51%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/http_stream.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/http_stream.cpp.o -MF CMakeFiles/darknetobjlib.dir/http_stream.cpp.o.d -o CMakeFiles/darknetobjlib.dir/http_stream.cpp.o -c /root/src/darknet/src-lib/http_stream.cpp\n",
            "[ 52%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/im2col.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/im2col.cpp.o -MF CMakeFiles/darknetobjlib.dir/im2col.cpp.o.d -o CMakeFiles/darknetobjlib.dir/im2col.cpp.o -c /root/src/darknet/src-lib/im2col.cpp\n",
            "[ 53%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/im2col_kernels.cu -o CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o\n",
            "[ 54%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/image.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/image.cpp.o -MF CMakeFiles/darknetobjlib.dir/image.cpp.o.d -o CMakeFiles/darknetobjlib.dir/image.cpp.o -c /root/src/darknet/src-lib/image.cpp\n",
            "[ 55%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o -MF CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o.d -o CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o -c /root/src/darknet/src-lib/image_opencv.cpp\n",
            "[ 56%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/layer.cpp.o -c /root/src/darknet/src-lib/layer.cpp\n",
            "[ 58%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/list.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/list.cpp.o -MF CMakeFiles/darknetobjlib.dir/list.cpp.o.d -o CMakeFiles/darknetobjlib.dir/list.cpp.o -c /root/src/darknet/src-lib/list.cpp\n",
            "[ 59%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/local_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/local_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/local_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/local_layer.cpp.o -c /root/src/darknet/src-lib/local_layer.cpp\n",
            "[ 60%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o -c /root/src/darknet/src-lib/lstm_layer.cpp\n",
            "[ 61%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/matrix.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/matrix.cpp.o -MF CMakeFiles/darknetobjlib.dir/matrix.cpp.o.d -o CMakeFiles/darknetobjlib.dir/matrix.cpp.o -c /root/src/darknet/src-lib/matrix.cpp\n",
            "[ 62%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o -c /root/src/darknet/src-lib/maxpool_layer.cpp\n",
            "[ 63%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/maxpool_layer_kernels.cu -o CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o\n",
            "[ 65%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/network.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/network.cpp.o -MF CMakeFiles/darknetobjlib.dir/network.cpp.o.d -o CMakeFiles/darknetobjlib.dir/network.cpp.o -c /root/src/darknet/src-lib/network.cpp\n",
            "[ 66%] \u001b[32mBuilding CUDA object src-lib/CMakeFiles/darknetobjlib.dir/network_kernels.cu.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP --options-file CMakeFiles/darknetobjlib.dir/includes_CUDA.rsp -O3 -DNDEBUG -std=c++17 -arch=native -Xcompiler=-fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/network_kernels.cu.o -MF CMakeFiles/darknetobjlib.dir/network_kernels.cu.o.d -x cu -c /root/src/darknet/src-lib/network_kernels.cu -o CMakeFiles/darknetobjlib.dir/network_kernels.cu.o\n",
            "[ 67%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/nightmare.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/nightmare.cpp.o -MF CMakeFiles/darknetobjlib.dir/nightmare.cpp.o.d -o CMakeFiles/darknetobjlib.dir/nightmare.cpp.o -c /root/src/darknet/src-lib/nightmare.cpp\n",
            "[ 68%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o -c /root/src/darknet/src-lib/normalization_layer.cpp\n",
            "[ 69%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/option_list.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/option_list.cpp.o -MF CMakeFiles/darknetobjlib.dir/option_list.cpp.o.d -o CMakeFiles/darknetobjlib.dir/option_list.cpp.o -c /root/src/darknet/src-lib/option_list.cpp\n",
            "[ 70%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/parser.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/parser.cpp.o -MF CMakeFiles/darknetobjlib.dir/parser.cpp.o.d -o CMakeFiles/darknetobjlib.dir/parser.cpp.o -c /root/src/darknet/src-lib/parser.cpp\n",
            "[ 72%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/region_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/region_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/region_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/region_layer.cpp.o -c /root/src/darknet/src-lib/region_layer.cpp\n",
            "[ 73%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o -c /root/src/darknet/src-lib/reorg_layer.cpp\n",
            "[ 74%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o -c /root/src/darknet/src-lib/reorg_old_layer.cpp\n",
            "[ 75%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o -c /root/src/darknet/src-lib/representation_layer.cpp\n",
            "[ 76%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/rnn.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/rnn.cpp.o -MF CMakeFiles/darknetobjlib.dir/rnn.cpp.o.d -o CMakeFiles/darknetobjlib.dir/rnn.cpp.o -c /root/src/darknet/src-lib/rnn.cpp\n",
            "[ 77%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o -c /root/src/darknet/src-lib/rnn_layer.cpp\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/rnn.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[K{anonymous}::float_pair get_rnn_data(unsigned char*, size_t*, int, size_t, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/rnn.cpp:107:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison is always false due to limited range of data type [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wtype-limits\u0007-Wtype-limits\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  107 |                         if(\u001b[01;35m\u001b[Kcurr > 255\u001b[m\u001b[K || curr <= 0 || next > 255 || next <= 0)\n",
            "      |                            \u001b[01;35m\u001b[K~~~~~^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/rnn.cpp:107:60:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison is always false due to limited range of data type [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wtype-limits\u0007-Wtype-limits\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  107 |                         if(curr > 255 || curr <= 0 || \u001b[01;35m\u001b[Knext > 255\u001b[m\u001b[K || next <= 0)\n",
            "      |                                                       \u001b[01;35m\u001b[K~~~~~^~~~~\u001b[m\u001b[K\n",
            "[ 79%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o -MF CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o.d -o CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o -c /root/src/darknet/src-lib/rnn_vid.cpp\n",
            "[ 80%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/route_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/route_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/route_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/route_layer.cpp.o -c /root/src/darknet/src-lib/route_layer.cpp\n",
            "[ 81%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o -c /root/src/darknet/src-lib/sam_layer.cpp\n",
            "[ 82%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o -c /root/src/darknet/src-lib/scale_channels_layer.cpp\n",
            "[ 83%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o -c /root/src/darknet/src-lib/shortcut_layer.cpp\n",
            "[ 84%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o -c /root/src/darknet/src-lib/softmax_layer.cpp\n",
            "[ 86%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/super.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/super.cpp.o -MF CMakeFiles/darknetobjlib.dir/super.cpp.o.d -o CMakeFiles/darknetobjlib.dir/super.cpp.o -c /root/src/darknet/src-lib/super.cpp\n",
            "[ 87%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/tag.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/tag.cpp.o -MF CMakeFiles/darknetobjlib.dir/tag.cpp.o.d -o CMakeFiles/darknetobjlib.dir/tag.cpp.o -c /root/src/darknet/src-lib/tag.cpp\n",
            "[ 88%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/tree.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/tree.cpp.o -MF CMakeFiles/darknetobjlib.dir/tree.cpp.o.d -o CMakeFiles/darknetobjlib.dir/tree.cpp.o -c /root/src/darknet/src-lib/tree.cpp\n",
            "[ 89%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o -c /root/src/darknet/src-lib/upsample_layer.cpp\n",
            "[ 90%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/utils.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/utils.cpp.o -MF CMakeFiles/darknetobjlib.dir/utils.cpp.o.d -o CMakeFiles/darknetobjlib.dir/utils.cpp.o -c /root/src/darknet/src-lib/utils.cpp\n",
            "[ 91%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/voxel.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/voxel.cpp.o -MF CMakeFiles/darknetobjlib.dir/voxel.cpp.o.d -o CMakeFiles/darknetobjlib.dir/voxel.cpp.o -c /root/src/darknet/src-lib/voxel.cpp\n",
            "[ 93%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/writing.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/writing.cpp.o -MF CMakeFiles/darknetobjlib.dir/writing.cpp.o.d -o CMakeFiles/darknetobjlib.dir/writing.cpp.o -c /root/src/darknet/src-lib/writing.cpp\n",
            "[ 94%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/yolo.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/yolo.cpp.o -MF CMakeFiles/darknetobjlib.dir/yolo.cpp.o.d -o CMakeFiles/darknetobjlib.dir/yolo.cpp.o -c /root/src/darknet/src-lib/yolo.cpp\n",
            "[ 95%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o -MF CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o.d -o CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o -c /root/src/darknet/src-lib/yolo_layer.cpp\n",
            "[ 96%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-lib && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIC -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -Ofast -MD -MT src-lib/CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o -MF CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o.d -o CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o -c /root/src/darknet/src-lib/yolo_v2_class.cpp\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[ 96%] Built target darknetobjlib\n",
            "make  -f src-lib/CMakeFiles/darknet.dir/build.make src-lib/CMakeFiles/darknet.dir/depend\n",
            "make  -f src-cli/CMakeFiles/darknetcli.dir/build.make src-cli/CMakeFiles/darknetcli.dir/depend\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-lib /root/src/darknet/build /root/src/darknet/build/src-lib /root/src/darknet/build/src-lib/CMakeFiles/darknet.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-cli /root/src/darknet/build /root/src/darknet/build/src-cli /root/src/darknet/build/src-cli/CMakeFiles/darknetcli.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknet.dir/build.make src-lib/CMakeFiles/darknet.dir/build\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make  -f src-cli/CMakeFiles/darknetcli.dir/build.make src-cli/CMakeFiles/darknetcli.dir/build\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "[ 98%] \u001b[32m\u001b[1mLinking CXX shared library libdarknet.so\u001b[0m\n",
            "[ 98%] \u001b[32mBuilding CXX object src-cli/CMakeFiles/darknetcli.dir/darknet.cpp.o\u001b[0m\n",
            "cd /root/src/darknet/build/src-cli && /usr/bin/c++ -DCUDNN -DCUDNN_HALF -DGPU -DOPENMP -Ddarknetcli_EXPORTS -I/root/src/darknet/src-cli -I/root/src/darknet/src-lib -isystem /usr/local/cuda/include -isystem /usr/include/opencv4 -O3 -DNDEBUG -std=gnu++17 -flto=auto -fno-fat-lto-objects -fPIE -fopenmp -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a -Wall -Wextra -Wno-unused-parameter -march=native -mtune=native -O3 -funsafe-math-optimizations -Wno-write-strings -Wno-unused-result -Wno-missing-field-initializers -Wno-ignored-qualifiers -Wno-sign-compare -fopenmp -MD -MT src-cli/CMakeFiles/darknetcli.dir/darknet.cpp.o -MF CMakeFiles/darknetcli.dir/darknet.cpp.o.d -o CMakeFiles/darknetcli.dir/darknet.cpp.o -c /root/src/darknet/src-cli/darknet.cpp\n",
            "cd /root/src/darknet/build/src-lib && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_link_script CMakeFiles/darknet.dir/link.txt --verbose=1\n",
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/usr/lib/x86_64-linux-gnu/libopencv_videoio.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_imgcodecs.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_objdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_calib3d.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_features2d.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_flann.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_photo.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_core.so.4.5.4d /usr/lib/gcc/x86_64-linux-gnu/11/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a -lcudadevrt -lcudart_static -lrt -lpthread -ldl \n",
            "[100%] \u001b[32m\u001b[1mLinking CXX executable darknet\u001b[0m\n",
            "cd /root/src/darknet/build/src-cli && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_link_script CMakeFiles/darknetcli.dir/link.txt --verbose=1\n",
            "/usr/bin/c++ -O3 -DNDEBUG -flto=auto -fno-fat-lto-objects -Wl,--export-dynamic -rdynamic CMakeFiles/darknetcli.dir/darknet.cpp.o \"../src-lib/CMakeFiles/darknetobjlib.dir/Chart.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/Timing.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/activation_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/activation_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/activations.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/art.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/avgpool_layer_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/batchnorm_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/blas.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/blas_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/box.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/captcha.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/cifar.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/classifier.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/coco.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/col2im.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/col2im_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/compare.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/connected_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/conv_lstm_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/convolutional_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/convolutional_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/cost_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/crnn_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/crop_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/crop_layer_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/dark_cuda.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/darknet_args_and_parms.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/darknet_cfg_and_state.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/darknet_format_and_colour.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/darknet_utils.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/data.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/demo.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/detection_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/detector.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/dice.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/dropout_layer_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/gaussian_yolo_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/gemm.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/go.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/gru_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/http_stream.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/im2col.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/im2col_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/image.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/image_opencv.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/list.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/local_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/lstm_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/matrix.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/maxpool_layer_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/network.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/network_kernels.cu.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/nightmare.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/normalization_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/option_list.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/parser.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/region_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/reorg_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/reorg_old_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/representation_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/rnn.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/rnn_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/rnn_vid.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/route_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/sam_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/scale_channels_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/shortcut_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/softmax_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/super.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/tag.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/tree.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/upsample_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/utils.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/voxel.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/writing.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/yolo.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/yolo_layer.cpp.o\" \"../src-lib/CMakeFiles/darknetobjlib.dir/yolo_v2_class.cpp.o\" -o darknet   -L/usr/local/cuda/targets/x86_64-linux/lib  -Wl,-rpath,/usr/local/cuda-12.2/targets/x86_64-linux/lib: /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudart.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/stubs/libcuda.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublas.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcurand.so /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/x86_64-linux-gnu/libopencv_stitching.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_alphamat.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_aruco.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_barcode.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_bgsegm.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_bioinspired.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ccalib.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn_objdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn_superres.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dpm.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_face.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_freetype.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_fuzzy.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_hdf.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_hfs.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_img_hash.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_intensity_transform.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_line_descriptor.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_mcc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_quality.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_rapid.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_reg.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_rgbd.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_saliency.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_shape.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_stereo.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_structured_light.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_superres.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_surface_matching.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_tracking.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_videostab.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_viz.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_wechat_qrcode.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_xobjdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_xphoto.so.4.5.4d /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublasLt.so /usr/local/cuda-12.2/targets/x86_64-linux/lib/libculibos.a /usr/lib/x86_64-linux-gnu/libopencv_highgui.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_datasets.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_plot.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_text.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ml.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_phase_unwrapping.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_optflow.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_ximgproc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_video.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_videoio.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_imgcodecs.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_objdetect.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_calib3d.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_dnn.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_features2d.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_flann.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_photo.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.4.5.4d /usr/lib/x86_64-linux-gnu/libopencv_core.so.4.5.4d /usr/lib/gcc/x86_64-linux-gnu/11/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a -lcudadevrt -lcudart_static -lrt -lpthread -ldl \n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/parser.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/parser.cpp:454:54:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Walloc-size-larger-than=\u0007-Walloc-size-larger-than=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  454 |                 classes_multipliers = (float *)calloc(classes_counters, sizeof(float));\n",
            "      |                                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/usr/include/stdlib.h:543:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n",
            "  543 | extern void *calloc (size_t __nmemb, size_t __size)\n",
            "      |              \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/parser.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/root/src/darknet/src-lib/parser.cpp:454:54:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Walloc-size-larger-than=\u0007-Walloc-size-larger-than=\u001b]8;;\u0007\u001b[m\u001b[K]\n",
            "  454 |                 classes_multipliers = (float *)calloc(classes_counters, sizeof(float));\n",
            "      |                                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K/usr/include/stdlib.h:543:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n",
            "  543 | extern void *calloc (size_t __nmemb, size_t __size)\n",
            "      |              \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[100%] Built target darknet\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[100%] Built target darknetcli\n",
            "make[1]: Leaving directory '/root/src/darknet/build'\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_progress_start /root/src/darknet/build/CMakeFiles 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confirm that the Darknet CLI was built correctly\n",
        "%cd ~/src/darknet/build\n",
        "!src-cli/darknet version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHJ9ez99W2jY",
        "outputId": "a63ba830-9dbc-43e6-85e0-82ac668cd8a1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/src/darknet/build\n",
            "Darknet \u001b[1;37mv2.0-225-g277ed9f4-dirty\u001b[0m\n",
            "CUDA runtime version 12020 (\u001b[1;37mv12.2\u001b[0m), driver version 12020 (\u001b[1;37mv12.2\u001b[0m)\n",
            "cuDNN version 12040 (\u001b[1;37mv9.1.1\u001b[0m), use of half-size floats is \u001b[1;37mENABLED\u001b[0m\n",
            "=> 0: \u001b[1;32mTesla T4\u001b[0m [#7.5], \u001b[1;33m14.7 GiB\u001b[0m\n",
            "OpenCV \u001b[1;37mv4.5.4\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and install the .deb installation package\n",
        "%cd ~/src/darknet/build\n",
        "!make package\n",
        "!sudo dpkg -i darknet-3*.deb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYlPy81cXDfx",
        "outputId": "31894a90-165c-4244-ea2d-0c08d72a8a28"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/src/darknet/build\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -S/root/src/darknet -B/root/src/darknet/build --check-build-system CMakeFiles/Makefile.cmake 0\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_progress_start /root/src/darknet/build/CMakeFiles /root/src/darknet/build//CMakeFiles/progress.marks\n",
            "make  -f CMakeFiles/Makefile2 all\n",
            "make[1]: Entering directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknetobjlib.dir/build.make src-lib/CMakeFiles/darknetobjlib.dir/depend\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-lib /root/src/darknet/build /root/src/darknet/build/src-lib /root/src/darknet/build/src-lib/CMakeFiles/darknetobjlib.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknetobjlib.dir/build.make src-lib/CMakeFiles/darknetobjlib.dir/build\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "make[2]: Nothing to be done for 'src-lib/CMakeFiles/darknetobjlib.dir/build'.\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[ 96%] Built target darknetobjlib\n",
            "make  -f src-lib/CMakeFiles/darknet.dir/build.make src-lib/CMakeFiles/darknet.dir/depend\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-lib /root/src/darknet/build /root/src/darknet/build/src-lib /root/src/darknet/build/src-lib/CMakeFiles/darknet.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make  -f src-lib/CMakeFiles/darknet.dir/build.make src-lib/CMakeFiles/darknet.dir/build\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "make[2]: Nothing to be done for 'src-lib/CMakeFiles/darknet.dir/build'.\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[ 97%] Built target darknet\n",
            "make  -f src-cli/CMakeFiles/darknetcli.dir/build.make src-cli/CMakeFiles/darknetcli.dir/depend\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "cd /root/src/darknet/build && /usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_depends \"Unix Makefiles\" /root/src/darknet /root/src/darknet/src-cli /root/src/darknet/build /root/src/darknet/build/src-cli /root/src/darknet/build/src-cli/CMakeFiles/darknetcli.dir/DependInfo.cmake \"--color=\"\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "make  -f src-cli/CMakeFiles/darknetcli.dir/build.make src-cli/CMakeFiles/darknetcli.dir/build\n",
            "make[2]: Entering directory '/root/src/darknet/build'\n",
            "make[2]: Nothing to be done for 'src-cli/CMakeFiles/darknetcli.dir/build'.\n",
            "make[2]: Leaving directory '/root/src/darknet/build'\n",
            "[100%] Built target darknetcli\n",
            "make[1]: Leaving directory '/root/src/darknet/build'\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E cmake_progress_start /root/src/darknet/build/CMakeFiles 0\n",
            "make  -f CMakeFiles/Makefile2 preinstall\n",
            "make[1]: Entering directory '/root/src/darknet/build'\n",
            "make[1]: Nothing to be done for 'preinstall'.\n",
            "make[1]: Leaving directory '/root/src/darknet/build'\n",
            "\u001b[36mRun CPack packaging tool...\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/cmake/data/bin/cpack --config ./CPackConfig.cmake\n",
            "CPack: Create package using DEB\n",
            "CPack: Install projects\n",
            "CPack: - Run preinstall target for: Darknet\n",
            "CPack: - Install project: Darknet []\n",
            "CPack: Create package\n",
            "CPackDeb: - Generating dependency list\n",
            "CPack: - package: /root/src/darknet/build/darknet-2.0.225-Linux.deb generated.\n",
            "(Reading database ... 123748 files and directories currently installed.)\n",
            "Preparing to unpack darknet-2.0.225-Linux.deb ...\n",
            "Unpacking darknet (2.0.225) over (2.0.225) ...\n",
            "Setting up darknet (2.0.225) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confirm that we can run \"darknet\" from anywhere since it has now been installed\n",
        "%cd ~\n",
        "!darknet version\n",
        "# Show some of the /opt/ files we've installed.  This should show ~40 .cfg files.\n",
        "!ls -lh /opt/darknet/cfg/yolov*"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hCOHoUFHXct7",
        "outputId": "f576b5b6-712c-4b5d-f9aa-0113bf81c677"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root\n",
            "Darknet \u001b[1;37mv2.0-225-g277ed9f4-dirty\u001b[0m\n",
            "CUDA runtime version 12020 (\u001b[1;37mv12.2\u001b[0m), driver version 12020 (\u001b[1;37mv12.2\u001b[0m)\n",
            "cuDNN version 12040 (\u001b[1;37mv9.1.1\u001b[0m), use of half-size floats is \u001b[1;37mENABLED\u001b[0m\n",
            "=> 0: \u001b[1;32mTesla T4\u001b[0m [#7.5], \u001b[1;33m14.7 GiB\u001b[0m\n",
            "OpenCV \u001b[1;37mv4.5.4\u001b[0m\n",
            "-rw-r--r-- 1 root root 2.7K Jun  7 13:44 /opt/darknet/cfg/yolov2.cfg\n",
            "-rw-r--r-- 1 root root 1.5K Jun  7 13:44 /opt/darknet/cfg/yolov2-tiny.cfg\n",
            "-rw-r--r-- 1 root root 1.5K Jun  7 13:44 /opt/darknet/cfg/yolov2-tiny-voc.cfg\n",
            "-rw-r--r-- 1 root root 2.7K Jun  7 13:44 /opt/darknet/cfg/yolov2-voc.cfg\n",
            "-rw-r--r-- 1 root root  11K Jun  7 13:44 /opt/darknet/cfg/yolov3_5l.cfg\n",
            "-rw-r--r-- 1 root root 8.2K Jun  7 13:44 /opt/darknet/cfg/yolov3.cfg\n",
            "-rw-r--r-- 1 root root 8.5K Jun  7 13:44 /opt/darknet/cfg/yolov3.coco-giou-12.cfg\n",
            "-rw-r--r-- 1 root root 8.2K Jun  7 13:44 /opt/darknet/cfg/yolov3-openimages.cfg\n",
            "-rw-r--r-- 1 root root 8.5K Jun  7 13:44 /opt/darknet/cfg/yolov3-spp.cfg\n",
            "-rw-r--r-- 1 root root 2.4K Jun  7 13:44 /opt/darknet/cfg/yolov3-tiny_3l.cfg\n",
            "-rw-r--r-- 1 root root 1.9K Jun  7 13:44 /opt/darknet/cfg/yolov3-tiny.cfg\n",
            "-rw-r--r-- 1 root root 1.9K Jun  7 13:44 /opt/darknet/cfg/yolov3-tiny_obj.cfg\n",
            "-rw-r--r-- 1 root root 2.2K Jun  7 13:44 /opt/darknet/cfg/yolov3-tiny_occlusion_track.cfg\n",
            "-rw-r--r-- 1 root root 2.1K Jun  7 13:44 /opt/darknet/cfg/yolov3-tiny-prn.cfg\n",
            "-rw-r--r-- 1 root root 2.0K Jun  7 13:44 /opt/darknet/cfg/yolov3-tiny_xnor.cfg\n",
            "-rw-r--r-- 1 root root 8.2K Jun  7 13:44 /opt/darknet/cfg/yolov3-voc.cfg\n",
            "-rw-r--r-- 1 root root 8.5K Jun  7 13:44 /opt/darknet/cfg/yolov3-voc.yolov3-giou-40.cfg\n",
            "-rw-r--r-- 1 root root  12K Jun  7 13:44 /opt/darknet/cfg/yolov4.cfg\n",
            "-rw-r--r-- 1 root root  14K Jun  7 13:44 /opt/darknet/cfg/yolov4-csp.cfg\n",
            "-rw-r--r-- 1 root root 9.3K Jun  7 13:44 /opt/darknet/cfg/yolov4-csp-s-mish.cfg\n",
            "-rw-r--r-- 1 root root  14K Jun  7 13:44 /opt/darknet/cfg/yolov4-csp-swish.cfg\n",
            "-rw-r--r-- 1 root root  16K Jun  7 13:44 /opt/darknet/cfg/yolov4-csp-x-mish.cfg\n",
            "-rw-r--r-- 1 root root  16K Jun  7 13:44 /opt/darknet/cfg/yolov4-csp-x-swish.cfg\n",
            "-rw-r--r-- 1 root root  16K Jun  7 13:44 /opt/darknet/cfg/yolov4-csp-x-swish-frozen.cfg\n",
            "-rw-r--r-- 1 root root  12K Jun  7 13:44 /opt/darknet/cfg/yolov4-custom.cfg\n",
            "-rw-r--r-- 1 root root  13K Jun  7 13:44 /opt/darknet/cfg/yolov4_new.cfg\n",
            "-rw-r--r-- 1 root root  19K Jun  7 13:44 /opt/darknet/cfg/yolov4-p5.cfg\n",
            "-rw-r--r-- 1 root root  19K Jun  7 13:44 /opt/darknet/cfg/yolov4-p5-frozen.cfg\n",
            "-rw-r--r-- 1 root root  24K Jun  7 13:44 /opt/darknet/cfg/yolov4-p6.cfg\n",
            "-rw-r--r-- 1 root root  15K Jun  7 13:44 /opt/darknet/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg\n",
            "-rw-r--r-- 1 root root 3.6K Jun  7 13:44 /opt/darknet/cfg/yolov4-tiny-3l.cfg\n",
            "-rw-r--r-- 1 root root 3.2K Jun  7 14:05 /opt/darknet/cfg/yolov4-tiny.cfg\n",
            "-rw-r--r-- 1 root root 4.8K Jun  7 13:44 /opt/darknet/cfg/yolov4-tiny_contrastive.cfg\n",
            "-rw-r--r-- 1 root root 3.0K Jun  7 13:44 /opt/darknet/cfg/yolov4-tiny-custom.cfg\n",
            "-rw-r--r-- 1 root root  15K Jun  7 13:44 /opt/darknet/cfg/yolov4x-mish.cfg\n",
            "-rw-r--r-- 1 root root  11K Jun  7 13:44 /opt/darknet/cfg/yolov7.cfg\n",
            "-rw-r--r-- 1 root root 7.4K Jun  7 13:44 /opt/darknet/cfg/yolov7-tiny.cfg\n",
            "-rw-r--r-- 1 root root  12K Jun  7 13:44 /opt/darknet/cfg/yolov7x.cfg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building and installing DarkHelp"
      ],
      "metadata": {
        "id": "7PfeMe9emfQc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build and install the DarkHelp CLI\n",
        "\n",
        "!sudo apt-get install build-essential libtclap-dev libmagic-dev libopencv-dev\n",
        "%cd ~/src\n",
        "%rm -rf ~/src/DarkHelp\n",
        "!git clone https://github.com/stephanecharette/DarkHelp.git\n",
        "%cd ~/src/DarkHelp\n",
        "%mkdir build\n",
        "%cd build\n",
        "!cmake -DCMAKE_BUILD_TYPE=Release ..\n",
        "!make -j $(nproc)\n",
        "!make package\n",
        "!sudo dpkg -i darkhelp*.deb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rkve8tJymxLe",
        "outputId": "3ab3864a-3b8a-4b65-b959-8fc90f1d7c25"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "build-essential is already the newest version (12.9ubuntu3).\n",
            "libopencv-dev is already the newest version (4.5.4+dfsg-9ubuntu4+jammy0).\n",
            "The following NEW packages will be installed:\n",
            "  libmagic-dev libtclap-dev\n",
            "0 upgraded, 2 newly installed, 0 to remove and 45 not upgraded.\n",
            "Need to get 2,683 kB of archives.\n",
            "After this operation, 15.3 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libmagic-dev amd64 1:5.41-3ubuntu0.1 [105 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libtclap-dev amd64 1.2.5-1 [2,578 kB]\n",
            "Fetched 2,683 kB in 1s (2,775 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 2.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package libmagic-dev:amd64.\n",
            "(Reading database ... 122045 files and directories currently installed.)\n",
            "Preparing to unpack .../libmagic-dev_1%3a5.41-3ubuntu0.1_amd64.deb ...\n",
            "Unpacking libmagic-dev:amd64 (1:5.41-3ubuntu0.1) ...\n",
            "Selecting previously unselected package libtclap-dev.\n",
            "Preparing to unpack .../libtclap-dev_1.2.5-1_amd64.deb ...\n",
            "Unpacking libtclap-dev (1.2.5-1) ...\n",
            "Setting up libmagic-dev:amd64 (1:5.41-3ubuntu0.1) ...\n",
            "Setting up libtclap-dev (1.2.5-1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "/root/src\n",
            "Cloning into 'DarkHelp'...\n",
            "remote: Enumerating objects: 1901, done.\u001b[K\n",
            "remote: Counting objects: 100% (403/403), done.\u001b[K\n",
            "remote: Compressing objects: 100% (266/266), done.\u001b[K\n",
            "remote: Total 1901 (delta 185), reused 177 (delta 119), pack-reused 1498\u001b[K\n",
            "Receiving objects: 100% (1901/1901), 10.06 MiB | 16.51 MiB/s, done.\n",
            "Resolving deltas: 100% (1206/1206), done.\n",
            "/root/src/DarkHelp\n",
            "/root/src/DarkHelp/build\n",
            "-- The C compiler identification is GNU 11.4.0\n",
            "-- The CXX compiler identification is GNU 11.4.0\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Check for working C compiler: /usr/bin/cc - skipped\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "\u001b[0mBuilding ver: 1.8.7-1\u001b[0m\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
            "-- Found Threads: TRUE  \n",
            "-- Found OpenCV: /usr (found version \"4.5.4\") \n",
            "-- Configuring done (0.5s)\n",
            "-- Generating done (0.1s)\n",
            "-- Build files have been written to: /root/src/DarkHelp/build\n",
            "[  2%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpConfig.cpp.o\u001b[0m\n",
            "[  4%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpNN.cpp.o\u001b[0m\n",
            "[  6%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpPositionTracker.cpp.o\u001b[0m\n",
            "[  9%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpPredictionResult.cpp.o\u001b[0m\n",
            "[ 11%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpThreads.cpp.o\u001b[0m\n",
            "[ 13%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelpUtils.cpp.o\u001b[0m\n",
            "[ 16%] \u001b[32mBuilding CXX object src-lib/CMakeFiles/dh.dir/DarkHelp_C_API.cpp.o\u001b[0m\n",
            "[ 18%] \u001b[32m\u001b[1mLinking CXX shared library libdarkhelp.so\u001b[0m\n",
            "[ 18%] Built target dh\n",
            "[ 23%] \u001b[32mBuilding CXX object src-tool/CMakeFiles/server.dir/DarkHelpServer.cpp.o\u001b[0m\n",
            "[ 23%] \u001b[32mBuilding CXX object src-tool/CMakeFiles/cli.dir/DarkHelpCli.cpp.o\u001b[0m\n",
            "[ 25%] \u001b[32m\u001b[1mLinking CXX executable DarkHelpServer\u001b[0m\n",
            "[ 25%] Built target server\n",
            "[ 27%] \u001b[32mBuilding CXX object src-tool/CMakeFiles/combine.dir/DarkHelpCombine.cpp.o\u001b[0m\n",
            "[ 30%] \u001b[32m\u001b[1mLinking CXX executable DarkHelp\u001b[0m\n",
            "[ 30%] Built target cli\n",
            "[ 32%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/display_single_image.dir/display_single_image.cpp.o\u001b[0m\n",
            "[ 34%] \u001b[32m\u001b[1mLinking CXX executable DarkHelpCombine\u001b[0m\n",
            "[ 34%] Built target combine\n",
            "[ 37%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/display_single_image_custom_settings.dir/display_single_image_custom_settings.cpp.o\u001b[0m\n",
            "[ 39%] \u001b[32m\u001b[1mLinking CXX executable display_single_image\u001b[0m\n",
            "[ 39%] Built target display_single_image\n",
            "[ 41%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/display_single_image_snapping.dir/display_single_image_snapping.cpp.o\u001b[0m\n",
            "[ 44%] \u001b[32m\u001b[1mLinking CXX executable display_single_image_custom_settings\u001b[0m\n",
            "[ 44%] Built target display_single_image_custom_settings\n",
            "[ 46%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/display_using_bundle.dir/display_using_bundle.cpp.o\u001b[0m\n",
            "[ 48%] \u001b[32m\u001b[1mLinking CXX executable display_single_image_snapping\u001b[0m\n",
            "[ 48%] Built target display_single_image_snapping\n",
            "[ 51%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/process_many_images_on_threads.dir/process_many_images_on_threads.cpp.o\u001b[0m\n",
            "[ 53%] \u001b[32m\u001b[1mLinking CXX executable display_using_bundle\u001b[0m\n",
            "[ 53%] Built target display_using_bundle\n",
            "[ 55%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/process_single_image.dir/process_single_image.cpp.o\u001b[0m\n",
            "[ 58%] \u001b[32m\u001b[1mLinking CXX executable process_many_images_on_threads\u001b[0m\n",
            "[ 58%] Built target process_many_images_on_threads\n",
            "[ 60%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/process_using_bundle_and_dhthreads.dir/process_using_bundle_and_dhthreads.cpp.o\u001b[0m\n",
            "[ 62%] \u001b[32m\u001b[1mLinking CXX executable process_single_image\u001b[0m\n",
            "[ 62%] Built target process_single_image\n",
            "[ 65%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/process_video_webcam.dir/process_video_webcam.cpp.o\u001b[0m\n",
            "[ 67%] \u001b[32m\u001b[1mLinking CXX executable process_using_bundle_and_dhthreads\u001b[0m\n",
            "[ 67%] Built target process_using_bundle_and_dhthreads\n",
            "[ 69%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/rotate_images.dir/rotate_images.cpp.o\u001b[0m\n",
            "[ 72%] \u001b[32m\u001b[1mLinking CXX executable process_video_webcam\u001b[0m\n",
            "[ 72%] Built target process_video_webcam\n",
            "[ 74%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/save_webcam_to_video.dir/save_webcam_to_video.cpp.o\u001b[0m\n",
            "[ 76%] \u001b[32m\u001b[1mLinking CXX executable rotate_images\u001b[0m\n",
            "[ 76%] Built target rotate_images\n",
            "[ 79%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/using_c_api.dir/using_c_api.cpp.o\u001b[0m\n",
            "[ 81%] \u001b[32m\u001b[1mLinking CXX executable save_webcam_to_video\u001b[0m\n",
            "[ 81%] Built target save_webcam_to_video\n",
            "[ 83%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/video_display_realtime.dir/video_display_realtime.cpp.o\u001b[0m\n",
            "[ 86%] \u001b[32m\u001b[1mLinking CXX executable using_c_api\u001b[0m\n",
            "[ 86%] Built target using_c_api\n",
            "[ 88%] \u001b[32mBuilding CXX object src-apps/CMakeFiles/video_object_counter.dir/video_object_counter.cpp.o\u001b[0m\n",
            "[ 90%] \u001b[32m\u001b[1mLinking CXX executable video_display_realtime\u001b[0m\n",
            "[ 90%] Built target video_display_realtime\n",
            "[ 93%] \u001b[32mBuilding CXX object src-cam/CMakeFiles/DarkHelp_cam.dir/Cam.cpp.o\u001b[0m\n",
            "[ 95%] \u001b[32m\u001b[1mLinking CXX executable video_object_counter\u001b[0m\n",
            "[ 95%] Built target video_object_counter\n",
            "[ 97%] \u001b[32mBuilding CXX object src-cam/CMakeFiles/DarkHelp_cam.dir/CamOptions.cpp.o\u001b[0m\n",
            "[100%] \u001b[32m\u001b[1mLinking CXX executable DarkHelp_cam\u001b[0m\n",
            "[100%] Built target DarkHelp_cam\n",
            "[ 18%] Built target dh\n",
            "[ 23%] Built target cli\n",
            "[ 27%] Built target server\n",
            "[ 32%] Built target combine\n",
            "[ 37%] Built target display_single_image\n",
            "[ 41%] Built target display_single_image_custom_settings\n",
            "[ 46%] Built target display_single_image_snapping\n",
            "[ 51%] Built target display_using_bundle\n",
            "[ 55%] Built target process_many_images_on_threads\n",
            "[ 60%] Built target process_single_image\n",
            "[ 65%] Built target process_using_bundle_and_dhthreads\n",
            "[ 69%] Built target process_video_webcam\n",
            "[ 74%] Built target rotate_images\n",
            "[ 79%] Built target save_webcam_to_video\n",
            "[ 83%] Built target using_c_api\n",
            "[ 88%] Built target video_display_realtime\n",
            "[ 93%] Built target video_object_counter\n",
            "[100%] Built target DarkHelp_cam\n",
            "\u001b[36mRun CPack packaging tool...\u001b[0m\n",
            "CPack: Create package using DEB\n",
            "CPack: Install projects\n",
            "CPack: - Run preinstall target for: DarkHelp\n",
            "CPack: - Install project: DarkHelp []\n",
            "CPack: Create package\n",
            "CPackDeb: - Generating dependency list\n",
            "CPack: - package: /root/src/DarkHelp/build/darkhelp-1.8.7-1-Linux-x86_64-Ubuntu-22.04.deb generated.\n",
            "Selecting previously unselected package darkhelp.\n",
            "(Reading database ... 123735 files and directories currently installed.)\n",
            "Preparing to unpack darkhelp-1.8.7-1-Linux-x86_64-Ubuntu-22.04.deb ...\n",
            "Unpacking darkhelp (1.8.7-1) ...\n",
            "Setting up darkhelp (1.8.7-1) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test the MSCOCO Weights"
      ],
      "metadata": {
        "id": "zo2LSLpUivXb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the MSCOCO pre-trained weights (https://github.com/hank-ai/darknet#mscoco-pre-trained-weights)\n",
        "%cd ~/src/darknet\n",
        "!wget --no-clobber https://github.com/hank-ai/darknet/releases/download/v2.0/yolov4-tiny.weights\n",
        "\n",
        "# This is what the command looks like when using Darknet to predict.  Because we're using --dont-show, the results will be saved as an image to \"predictions.jpg\"\n",
        "#!darknet detector test --dont-show cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights artwork/dog.jpg\n",
        "\n",
        "# And this how we do the equivalent but with DarkHelp instead.  See the DarkHelp documentation to see all the options which alter the look of the output image.\n",
        "# https://www.ccoderun.ca/darkhelp/api/Parameters.html\n",
        "!DarkHelp --json --autohide off --keep --duration off --fontscale 0.7 --line 5 --shade 0.25 --threshold 0.5 --outdir . cfg/yolov4-tiny.cfg cfg/coco.names yolov4-tiny.weights artwork/dog.jpg\n",
        "\n",
        "# Display the results\n",
        "import cv2 as cv2\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "img = cv2.imread('dog.png')\n",
        "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fmMQ8S_XjEzX",
        "outputId": "410d8513-364c-444f-c985-798d27b1e160"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/src/darknet\n",
            "File ‘yolov4-tiny.weights’ already there; not retrieving.\n",
            "\n",
            "-> config file:  cfg/yolov4-tiny.cfg\n",
            "-> weights file: yolov4-tiny.weights\n",
            "-> names file:   cfg/coco.names\n",
            "-> driver:       Darknetd\n",
            " Try to load cfg: cfg/yolov4-tiny.cfg, weights: yolov4-tiny.weights, clear = 1 \n",
            " 0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4 \n",
            "net.optimized_memory = 0 \n",
            "mini_batch = 1, batch = 1, time_steps = 1, train = 0 \n",
            "   layer   filters  size/strd(dil)      input                output\n",
            "   0 Create CUDA-stream - 0 \n",
            " Create cudnn-handle 0 \n",
            "conv     32       3 x 3/ 2    416 x 416 x   3 ->  208 x 208 x  32 0.075 BF\n",
            "   1 conv     64       3 x 3/ 2    208 x 208 x  32 ->  104 x 104 x  64 0.399 BF\n",
            "   2 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n",
            "   3 route  2 \t\t                       1/2 ->  104 x 104 x  32 \n",
            "   4 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF\n",
            "   5 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF\n",
            "   6 route  5 4 \t                           ->  104 x 104 x  64 \n",
            "   7 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n",
            "   8 route  2 7 \t                           ->  104 x 104 x 128 \n",
            "   9 max                2x 2/ 2    104 x 104 x 128 ->   52 x  52 x 128 0.001 BF\n",
            "  10 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n",
            "  11 route  10 \t\t                       1/2 ->   52 x  52 x  64 \n",
            "  12 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF\n",
            "  13 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF\n",
            "  14 route  13 12 \t                           ->   52 x  52 x 128 \n",
            "  15 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n",
            "  16 route  10 15 \t                           ->   52 x  52 x 256 \n",
            "  17 max                2x 2/ 2     52 x  52 x 256 ->   26 x  26 x 256 0.001 BF\n",
            "  18 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n",
            "  19 route  18 \t\t                       1/2 ->   26 x  26 x 128 \n",
            "  20 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF\n",
            "  21 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF\n",
            "  22 route  21 20 \t                           ->   26 x  26 x 256 \n",
            "  23 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n",
            "  24 route  18 23 \t                           ->   26 x  26 x 512 \n",
            "  25 max                2x 2/ 2     26 x  26 x 512 ->   13 x  13 x 512 0.000 BF\n",
            "  26 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n",
            "  27 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n",
            "  28 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF\n",
            "  29 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF\n",
            "  30 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            "  31 route  27 \t\t                           ->   13 x  13 x 256 \n",
            "  32 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF\n",
            "  33 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128\n",
            "  34 route  33 23 \t                           ->   26 x  26 x 384 \n",
            "  35 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF\n",
            "  36 conv    255       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 255 0.088 BF\n",
            "  37 yolo\n",
            "[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n",
            "nms_kind: greedynms (1), beta = 0.600000 \n",
            "Total BFLOPS 6.910 \n",
            "avg_outputs = 310203 \n",
            "Allocating workspace to transfer between CPU and GPU:  25.0 MiB\n",
            " Try to load weights: yolov4-tiny.weights \n",
            "Loading weights from yolov4-tiny.weights...\n",
            " seen 64, trained: 0 K-images (0 Kilo-batches_64) \n",
            "Done! Loaded 38 layers from weights-file \n",
            "-> loading network took 522.797 milliseconds\n",
            "-> neural network dimensions: 416x416\n",
            "-> output directory: .\n",
            "-> looking for image and video files\n",
            "-> found 1 file\n",
            "setting message: \"press 'h' for help\"\n",
            "#1/1: loading \"artwork/dog.jpg\"\n",
            "-> prediction took 57.887 milliseconds\n",
            "-> prediction results: 3\n",
            "-> 1/3: \"bicycle 61%\" #1 prob=0.605251 x=72 y=100 w=505 h=379 oid=0 tile=0 entries=1\n",
            "-> 2/3: \"truck 81%\" #7 prob=0.814678 x=464 y=80 w=240 h=91 oid=0 tile=0 entries=1\n",
            "-> 3/3: \"dog 87%\" #16 prob=0.870107 x=137 y=206 w=182 h=336 oid=0 tile=0 entries=1\n",
            "-> annotated image saved to \"./dog.png\"\n",
            "JSON OUTPUT\n",
            "{\n",
            "    \"argv\": \"DarkHelp --json --autohide off --keep --duration off --fontscale 0.7 --line 5 --shade 0.25 --threshold 0.5 --outdir . cfg/yolov4-tiny.cfg cfg/coco.names yolov4-tiny.weights artwork/dog.jpg\",\n",
            "    \"file\": [\n",
            "        {\n",
            "            \"annotated_image\": \"./dog.png\",\n",
            "            \"count\": 3,\n",
            "            \"duration\": \"57.887 milliseconds\",\n",
            "            \"filename\": \"artwork/dog.jpg\",\n",
            "            \"original_height\": 576,\n",
            "            \"original_width\": 768,\n",
            "            \"prediction\": [\n",
            "                {\n",
            "                    \"all_probabilities\": [\n",
            "                        {\n",
            "                            \"class\": 1,\n",
            "                            \"name\": \"bicycle\",\n",
            "                            \"probability\": 0.6052505970001221\n",
            "                        }\n",
            "                    ],\n",
            "                    \"best_class\": 1,\n",
            "                    \"best_probability\": 0.6052505970001221,\n",
            "                    \"name\": \"bicycle 61%\",\n",
            "                    \"original_point\": {\n",
            "                        \"x\": 0.4220893979072571,\n",
            "                        \"y\": 0.5027541518211365\n",
            "                    },\n",
            "                    \"original_size\": {\n",
            "                        \"height\": 0.6584054827690125,\n",
            "                        \"width\": 0.6575257182121277\n",
            "                    },\n",
            "                    \"prediction_index\": 0,\n",
            "                    \"rect\": {\n",
            "                        \"height\": 379,\n",
            "                        \"width\": 505,\n",
            "                        \"x\": 72,\n",
            "                        \"y\": 100\n",
            "                    }\n",
            "                },\n",
            "                {\n",
            "                    \"all_probabilities\": [\n",
            "                        {\n",
            "                            \"class\": 7,\n",
            "                            \"name\": \"truck\",\n",
            "                            \"probability\": 0.8146780133247375\n",
            "                        }\n",
            "                    ],\n",
            "                    \"best_class\": 7,\n",
            "                    \"best_probability\": 0.8146780133247375,\n",
            "                    \"name\": \"truck 81%\",\n",
            "                    \"original_point\": {\n",
            "                        \"x\": 0.7607223987579346,\n",
            "                        \"y\": 0.21737588942050934\n",
            "                    },\n",
            "                    \"original_size\": {\n",
            "                        \"height\": 0.15820147097110748,\n",
            "                        \"width\": 0.31217437982559204\n",
            "                    },\n",
            "                    \"prediction_index\": 1,\n",
            "                    \"rect\": {\n",
            "                        \"height\": 91,\n",
            "                        \"width\": 240,\n",
            "                        \"x\": 464,\n",
            "                        \"y\": 80\n",
            "                    }\n",
            "                },\n",
            "                {\n",
            "                    \"all_probabilities\": [\n",
            "                        {\n",
            "                            \"class\": 16,\n",
            "                            \"name\": \"dog\",\n",
            "                            \"probability\": 0.8701072335243225\n",
            "                        }\n",
            "                    ],\n",
            "                    \"best_class\": 16,\n",
            "                    \"best_probability\": 0.8701072335243225,\n",
            "                    \"name\": \"dog 87%\",\n",
            "                    \"original_point\": {\n",
            "                        \"x\": 0.29713550209999084,\n",
            "                        \"y\": 0.6486532688140869\n",
            "                    },\n",
            "                    \"original_size\": {\n",
            "                        \"height\": 0.5840701460838318,\n",
            "                        \"width\": 0.23761819303035736\n",
            "                    },\n",
            "                    \"prediction_index\": 2,\n",
            "                    \"rect\": {\n",
            "                        \"height\": 336,\n",
            "                        \"width\": 182,\n",
            "                        \"x\": 137,\n",
            "                        \"y\": 206\n",
            "                    }\n",
            "                }\n",
            "            ],\n",
            "            \"resized_height\": 576,\n",
            "            \"resized_width\": 768,\n",
            "            \"tiles\": {\n",
            "                \"height\": 416,\n",
            "                \"horizontal\": 1,\n",
            "                \"vertical\": 1,\n",
            "                \"width\": 416\n",
            "            },\n",
            "            \"timestamp\": {\n",
            "                \"epoch\": 1717769164,\n",
            "                \"nanoseconds\": 1717769164762118227,\n",
            "                \"text\": \"2024-06-07 14:06:04 +0000\"\n",
            "            },\n",
            "            \"type\": \"image/jpeg\"\n",
            "        }\n",
            "    ],\n",
            "    \"network\": {\n",
            "        \"cfg\": \"cfg/yolov4-tiny.cfg\",\n",
            "        \"loading\": \"522.797 milliseconds\",\n",
            "        \"names\": \"cfg/coco.names\",\n",
            "        \"weights\": \"yolov4-tiny.weights\"\n",
            "    },\n",
            "    \"settings\": {\n",
            "        \"driver\": \"darknet\",\n",
            "        \"enable_tiles\": false,\n",
            "        \"force_greyscale\": false,\n",
            "        \"hierarchy\": 0.5,\n",
            "        \"include_percentage\": true,\n",
            "        \"keep_annotations\": true,\n",
            "        \"nms\": 0.44999998807907104,\n",
            "        \"outdir\": \".\",\n",
            "        \"output_redirection\": false,\n",
            "        \"snapping\": false,\n",
            "        \"threshold\": 0.5\n",
            "    },\n",
            "    \"timestamp\": {\n",
            "        \"epoch\": 1717769164,\n",
            "        \"text\": \"2024-06-07 14:06:04 +0000\"\n",
            "    }\n",
            "}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6289befc40>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# All done!\n",
        "\n",
        "At this point, you'd need to upload your dataset, and then call `darknet detector train ...` to begin training your neural network.\n",
        "\n",
        "Also see:\n",
        "- https://www.ccoderun.ca/programming/yolo_faq/#training_command\n",
        "- https://github.com/hank-ai/darknet#training"
      ],
      "metadata": {
        "id": "_pTAcXQce8kY"
      }
    }
  ]
}
